View Planning for 3D Reconstruction Using Time-of-Flight Camera Data
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Developing visual sensing strategies through next best view planning
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Optimal view path planning for visual SLAM
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Covariance propagation and next best view planning for 3d reconstruction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
A probabilistic framework for next best view estimation in a cluttered environment
Journal of Visual Communication and Image Representation
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We present an algorithm for optimal view point selection for 3-D reconstruction of an object using 2-D image points. Since the image points are noisy, a Kalman filter is used to obtain the best estimate of the object's geometry. This Kalman filter allows us to efficiently predict the effect of any given camera position on the uncertainty, and therefore quality, of the estimate. By choosing a suitable optimization criterion, we are able to determine the camera positions which minimize our reconstruction error. We verify our results using two experiments with real images: one experiment uses a calibration pattern for comparison to a ground-truth state, the other reconstructs a real world object.